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You know that understanding your users is an absolutely crucial part of creating better products and services that your users actually want, returning to time and time again. But how can you truly understand the needs and wants of your users, keeping up with trends over time or recognizing patterns and trends as they emerge? With data, of course.
As artificial intelligence was originally introduced to data analysis, there were a few set programs and algorithms that were a ‘one size fits all’ model; they were adapted to the specific needs of users but lacked the flexibility and customization that would have had truly transformative effects.
This is exactly where data science and artificial intelligence come into the question. While data analysis focuses primarily on making sense of the data once it’s been processed and making it visually presentable to stakeholders, data science handles the actual creation of the systems used to collect, sort, analyze, and draw conclusions about the data.
Together with AI, the field of data science has expanded rapidly, absolutely transforming what we can do with data when AI is involved. And in this article, we’ll dive into how data science and artificial intelligence have come together to transform our understanding of data and the best skills and tools to add to your CV.
Data Science & Artificial Intelligence
Although they’re frequently conflated as they’re used for similar purposes these days, there are a few key differences that are essential to understanding how the two work together to create the best possible, data-empowering result:
Their definitions:
Data science is focused on finding answers from both structured and unstructured data sets, using statistical analysis and machine learning to create systems to properly analyze data.
Artificial intelligence focuses more on programming computers to mimic human intelligence, training them to make decisions like humans would and making the right decisions based on available information.
Their scopes:
The field of data science is focused specifically on outcomes from analyzing data and identifying patterns and trends, all the while creating systems that analyze large quantities of data quickly and efficiently.
The field of artificial intelligence, on the other hand, primarily thinks about how to best train machines to understand the entirety of their surroundings, much like people, to make the most human-like decisions.
Their methodologies:
Statistical analysis, machine learning, data mining, and data analysis are all important pillars of the data science process.
Machine learning, natural language processing, robotics, and computer vision all make up the field of artificial intelligence.
Their goals:
Data science seeks to use their systems and algorithms to gain insight from data, building and adjusting said systems as needed to obtain the best possible results.
Artificial intelligence wishes to train systems and computers well enough so that they’re able to make human-like decisions on their own and adapt to changing circumstances.
Their uses:
Data science is frequently employed in a wide range of sectors as more and more companies are realizing the value of creating their own systems to analyze data exactly pertaining to their needs.
Artificial intelligence is becoming increasingly popular in a wide range of sectors as well, with its efficiency and automation becoming a grand selling point for companies wishing to take advantage of all it offers.
How data science and artificial intelligence work together
As you can see, data science and AI overlap quite a bit and that’s because there are quite a few similarities between the two and their overall goals. They also play intricate roles in the development of the other; for example, while data science seeks to properly extract insights and patterns from data, data is an absolutely crucial aspect of training AI systems to work or act a certain way.
Because of this heavy dependence on data, both use machine learning, statistical analysis, and data mining to achieve their results and have similar desired outcomes: they want to use data to improve overall processes and achieve their goals.
As the world becomes more and more dependent on data, their applications will also continue to conflate, with an increasingly high number of sectors choosing to prioritize both data science and artificial intelligence.
Now that you understand the similarities between the two and how they work in a complementary fashion, let’s dive right into some of the skills and tools you can master to prepare yourself to enter the field of artificial intelligence using your data science skills.
Skills and Tools for AI Data Science
To make the most of the potential that the combination of data science and artificial intelligence bring to the table, adding the following skills and tools to your repertoire will make you an incredible candidate.
Programming languages
The back of any AI or data science tool is a programming language; the specific language used by developers defines the code on which the tool is built and if you’re interested in creating your own tool, gaining a solid proficiency in common AI programming languages, such as Python, Java, C++, and SQL.
Although all of these are common programming languages in the field of AI, the most common one is Python so if you’re looking to hit the ground running, that’s definitely the way to go.
Data analytics
In order to make the most of your data models, you’ll need to know how to understand the data in front of you and learning how to use data tools such as SAS, R, and Hive will help propel your data skills in the right direction.
Don’t just stop at the actual data analysis step: investing in your data cleaning and organizing skills can help you towards the beginning of your project and a solid knowledge of data visualization and assist you in displaying your findings to stakeholders.
Deep learning & natural language processing
The main focus of the field of artificial intelligence is reaching a point where machines can understand, respond to, and react like humans and deep learning is a key part of that, along with natural language processing.
When you begin to focus on specific areas of AI, such as the comprehension of human speech when accents, slang, and dialects come into the picture, you’ll understand the complexities of creating tools that can deliver the desired outputs in just a matter of seconds.
Statistics
You know that data is key to making good decisions, but how can you take into account the ‘maybes’ of data, such as faulty data, human error, response bias, and uncertainty? Well, to work in data science, you’ll have to have a solid understanding of statistics and its surrounding fields, such as probability and decision making.
We’ll never be able to accurately predict what someone will do 100% of the time because there are so many changing variables, but a solid foundation in statistics will ensure that you cover all your bases when reaching conclusions.
DevOps
Tech teams are expanding rapidly and more and more team leaders are struggling to keep everyone on the same page while delivering on-time and high quality results. That’s where DevOps comes in; it’s a methodology used within software development that focuses on teamwork and communication.
As you can see, there are quite a few skills you can add to your to-do list if you’re looking to harness the power of AI and transform your career with the help of data.
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